QSPy: Quantitative Systems Pharmacology in Python

QSPy ('Cue Ess Pie') is a Python-based framework for the programmatic construction of rule-based mathematical models that describe drugs and their interactions with biological systems. Built on PySB, it enables modular modeling and simulation of quantitative systems pharmacology (QSP) models.

💊 💻

What's new in

version 0.1.0

• First release!

Table of Contents

1. Install
   1. Dependencies
   2. pip install
   3. Manual install
2. License
3. Change Log
4. Documentation and Usage
   1. Quick Overview
   2. Example
5. Contact
6. Contributing
7. Supporting
8. Other Useful Tools

Install

qspy has been developed with Python 3.11.9 and PySB 1.15.0.

Dependencies

QSPy has the following core dependencies:

• PySB
• pysb-pkpd
• pysb-units
• Microbench
• PyViPR
• MerGram
• toml
• seaborn

Installation

1. Install PySB using conda or mamba:

conda install -c alubbock pysb

OR

mamba install -c alubbock pysb

2. Install qspy with pip:

pip install cueesspie

Testing and Coverage

For automated testing and coverage analysis:

• pytest
• Coverage.py
• nose

pip install pytest coverage nose

License

This project is licensed under the BSD 2-Clause License - see the LICENSE file for details

Change Log

See: CHANGELOG

Documentation and Usage

Full documentation is available at:

https://borealis-biomodeling.github.io/qspy/

Built With:

Quick Start Example

from qspy import Model, parameters, monomers, rules, initials, observables
from qspy.functionaltags import PROTEIN, DRUG
from qspy.validation import ModelMetadataTracker, ModelChecker

Model(name="SimpleQSP").with_units(concentration='nM', time='1/s', volume='L')

with parameters():
    k_f = (1.0, "1/min")
    k_r = (0.5, "1/min")
    L_0 = (100.0, "nM")
    R_0 = (10.0, "nM")

with monomers():
    L = (["b"], {}, DRUG.AGONIST)
    R = (["b", 'active'], {'active':[False, True]}, PROTEIN.RECEPTOR)

with rules():
    bind = (L(b=None) + R(b=None, active=False) | L(b=1) % R(b=1, active=True), k_f, k_r)

with initials():
    L(b=None) << L_0
    R(b=None, active=False) << R_0

with observables():
    L() > "L_total"
    R() > "R_total"
    R(active=True) > "R_active"

# Track and export model metadata
ModelMetadataTracker(version="1.0.0", author="Alice", export_toml=True)

# Run model validation checks
ModelChecker()

# Generate a Markdown summary of the model
model.markdown_summary()
               

Contact

• Issues 🐛 : Please open a GitHub Issue to report any problems/bugs with the code or its execution, or to make any feature requests.
• Discussions ❔ : If you have questions, suggestions, or want to discuss anything else related to the project, feel free to use the Discussions board.
• Support ❓ : For any other support inquiries you can send an email to blakeaw1102@gmail.com.

Contributing

Interested in contributing to this project? See CONTRIBUTING for details.

Supporting

I'm very happy that you've chosen to use qspy. This add-on is a project that I develop and maintain on my own time, independently of the core PySB library, and without external funding. If you've found it helpful, here are a few ways you can support its ongoing development:

• Star ⭐ : Show your support by starring the GitHub repository. It helps increase the project's visibility and lets others know it's useful. It also benefits my motivation to continue improving the package!
• Share 📣 : Sharing qspy on your social media, forums, or with your network is another great way to support the project. It helps more people discover qspy, which in turn motivates me to keep developing!
• Cite 📚 : Citing or mentioning this software in your work, publications, or projects is another valuable way to support it. It helps spread the word and acknowledges the effort put into its development, which is greatly appreciated!
• Sponsor 💵 : Even small financial contributions, such as spotting me the cost of a tea through Ko-fi so I can get my caffeine fix, can make a big difference! Every little bit can help me continue developing this and other open-source projects.

Acknowledegments

Special thanks for Martin Breuss's MkDocs tuorial, which served as the template for setting up and generating documentation using Mkdocs.

AI Acknowledgement

This package was developed with AI assistance. This inlcudes the generative AI tools ChatGPT, Microsoft Copilot, and GitHub Copilot, which were used to brainstorm features and implementation details, draft initial code snippets and boilerplate, and support documentation through outlining, editing, and docstring generation.